The invention relates to a face classification system. The invention further relates to a security system capable of identifying persons using face classification.
Any method of face classification divides naturally into two stages; a) Face location and b) Classification.
A survey of the use of face recognition for security applications by M. Nixon, "Automated Facial Recognition and its Potential for Security" IEE Coloquium on "MMI in Computer Security", (Digest No. 80) (1986), classifies face recognition techniques as either statistical or structural in side view or front view. For front view the structural techniques are further classified in terms of feature measurements and angular measurements.
Any system which is to operate in an unconstrained environment, must use a structural approach which incorporates some knowledge of the structure of a face so that features may still be located under varying lighting conditions, background and pose. Metrics are constructed based upon the relationships between located facial features, and are then used to classify the face. One problem with this approach is choosing appropriate metrics. For example, R. Buhr, "Front Face Analysis and Classification (Analyse und Klassification von Geischtsbildern)", ntz Archiv 8, No. 10, pp 245-256 (1986) proposed using 45 measures. Additionally, for such an approach it is necessary to locate the facial features with high precision. Other examples of this approach are exemplified by R. J. Baron, "Mechanism of Human Facial Recognition", Int. J. Man-Machine Studies, 15, pp 137-178 (1981) and T. Sakai, M. Nagao & M. Kanade, "Computer Analysis and Classification of Photographs of Human Faces", Proc. 1st USA-Japan Computer Conf. AFIPS Press, New Jersey, pp 55-62 (1972).
The attraction of the statistical approach is the possibility that simple methods may be employed to extract feature vectors. I. Aleksander, W. V. Thomas & P. A. Bowden have disclosed in "A Step Forward in Image Recognition", Sensor Review, July, pp120-124 (1984) a statistical face recognition system, using WISARD, which is able to recognize a pattern within one frame period. The main shortcoming of this system is that it is specific to a particular position and orientation, so the individual's characteristics must be learnt for a series of spatial displacements, reducing the reliability of the identification and reducing the storage capacity of the system.
A departure from the either wholly statistical or wholly structural approaches is disclosed by I. Craw & P. Cameron, "Parameterising Images for Recognition and Reconstruction", BMVC 91, pp 367-370, Springer-Verlag (1991) which uses a hybrid structural/statistical approach, in which a large number of feature points are utilized to normalize the shape of the face, and then principal component analysis is used to obtain a lower dimensional representation of the face. This is compared with a database of similarly encoded faces for recognition purposes. Such a hybrid approach offers the advantage of not having to constrain the face unduly (structural), and also retains the significant advantages of statistical methods.